SGMIEC: using selfish gene theory to construct mutualinformation and entropy based cluster for optimization

ABSTRACT

This paper proposes a new approach named SGMIEC in the field of Estimation of Distribution Algorithm (EDA). While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, the Selfish Gene Theory (SG) is deployed in this approach and a Mutual Information and Entropy based Cluster (MIEC) model with an incremental learning and resample scheme is also set to optimize the probability distribution of the virtual population. Experimental results on several benchmark problems demonstrate that, compared with BMDA and COMIT , SGMIEC often performs better in convergent reliability, convergent velocity and convergent process.

REFERENCES

• 1: Thomas M. Cover , Joy A. Thomas, Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing), Wiley-Interscience, 2006
• 2: Fulvio Corno , Matteo Sonza Reorda , Giovanni Squillero, The selfish gene algorithm: a new evolutionary optimization strategy, Proceedings of the 1998 ACM symposium on Applied Computing, p.349-355, February 27-March 01, 1998, Atlanta, Georgia, United States [doi>10.1145/330560.330838]
• 3: G. Harik. Linkage learning via probabilistic modeling in the ecga. Technical report, University of Illinois at Urbana-Champaign, 1999.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

General Terms:
Algorithms

Keywords:
estimation of distribution algorithms, mutual information, selfish gene theory